Publications by authors named "G Cleland"

The aim of this study is to ascertain whether a simplified screening algorithm incorporating glycated haemoglobin (HbA1c) tests increases type 2 diabetes (T2D) screening in 10- to 14-year-old Aboriginal Australians presenting to primary healthcare (PHC) services. The study involved a 6-month pilot of a locally developed evidence-based screening algorithm in a remote Western Australian Kimberley town. A retrospective audit of electronic health records for the pilot period (27 June-26 December 2016) and a 6-month period before the screening algorithm was introduced (1 October 2015-31 March 2016) was conducted.

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We developed a metabolomics workflow using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry to determine the effect of thermal treatment on milk composition and metabolites based on multivariate data analysis. We analyzed raw, pasteurized, and UHT milk samples. The samples were first centrifuged to remove the fat layer and mixed with methanol to precipitate proteins.

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Aim: Haemophilus influenzae continues to cause invasive disease in children despite widespread Hib immunisation. The significance of non-B serotypes continues to be investigated, with evidence of increased invasive non-typeable H. influenzae (NTHi) world-wide.

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Background: Complications of scabies and impetigo such as glomerulonephritis and invasive bacterial infection in Australian Aboriginal children remain significant problems and the overall global burden of disease attributable to these skin infections remains high despite the availability of effective treatment. We hypothesised that one factor contributing to this high burden is that skin infection is under-recognised and hence under-treated, in settings where prevalence is high.

Methods: We conducted a prospective, cross-sectional study to assess the burden of scabies, impetigo, tinea and pediculosis in children admitted to two regional Australian hospitals from October 2015 to January 2016.

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The use of collision cross-section (CCS) values obtained by ion mobility high-resolution mass spectrometry has added a third dimension (alongside retention time and exact mass) to aid in the identification of compounds. However, its utility is limited by the number of experimental CCS values currently available. This work demonstrates the potential of artificial neural networks (ANNs) for the prediction of CCS values of pesticides.

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